DAILY PRECIPITATION FROM STATISTICAL RECONSTRUCTIONS (Serreze)

Summary

Providing rapid updates of gridded precipitation fields is difficult due to the degradation of the station network. Starting about 1990, many stations in the Former Soviet Union (FSU) and Canada closed. Canada is also seeing a trend toward automation. In recognition, we are providing updated fields using statistical techniques. This reconstruction technique has also been used to compile fields back to 1980 to provide a period of overlap with gridded fields based on interpolation of monthly station precipitation and daily disaggregation (see DAILY PRECIPITATION, FROM MONTHLY STATION RECORDS AND DISAGGREGATION). The reconstructions employ a four-step approach. As part of the data processing of ArcticRIMS (http://RIMS.unh.edu) based at the Water Systems Analysis Group, University of New Hampshire all data sets have been aggregated to multiple temporal and spatial resolutions.

The data is presented in 24 sub-datasets of different spatial and temporal aggregations (see aggregation description below).

Data Access

ORDER data for delivery by FTP.

* - This Dataset was submitted to or acquired by ArcticRIMS from a third party Research Institution.

Data Aggregation

There are two distinct methods to produce data aggregation along the "time" axis of the cube. These are -

1. Mean. Monthly or yearly values for each grid cell in the dataset has been produced as simple mean (average) over the indicated time period. An example is temperature datasets.

2. Sum. Monthly or yearly values for each grid cell in the dataset has been produced as arithmetic total over the indicated time period. An example is precipitation datasets.

3. Vector. Monthly or yearly values for each grid cell in the dataset has been produced as product vector (vector average) over the indicated time period. An example is wind direction datasets.

4. Class. Monthly or yearly values for each grid cell in the dataset has been produced as prevailing (most frequent) qualitative class over the indicated time period. An example is freeze-thaw datasets.

Each aggregated data file in a dataset has a notation of a method (mean or sum) used to produce the file. The notation included in the first (top) line of the file. Please, pay attention to that.

Aggregation along the "spatial scale" axis of the cube is done using simple mean (average) over all grid cells of each basin (river, sea basin, or continent) from an appropriate grid cell file.

 

Additional Information

Temporal Coverage

Spatial Coverage

Point of Contact